Efficient SN-like and PN-like Dynamic Low Rank methods for Thermal Radiative Transfer
Terry Haut, John Loffeld, Lukas Einkemmer, Pierson Guthrey, Stefan Brunner, William Schill

TL;DR
This paper introduces new SN-like and PN-like Dynamic Low Rank methods for thermal radiative transfer that significantly reduce computational costs and angular artifacts, making them practical for complex, real-world simulations.
Contribution
The paper develops novel SN-like and PN-like DLR methods that improve efficiency and practicality of TRT simulations, leveraging low-rank representations and optimized transport kernels.
Findings
Significant reduction in angular artifacts compared to traditional methods.
Comparable computational cost to standard SN methods.
Effective handling of highly heterogeneous 2D problems.
Abstract
Dynamic Low Rank (DLR) methods are a promising way to reduce the computational cost and memory footprint of the high-dimensional thermal radiative transfer (TRT) equations. The TRT equations are a system of nonlinear PDEs that model the energy exhchange between the material temperature and the radiation energy density; due to their high dimensionality, solving the TRT equations is often bottleneck in multi-physics simulations. DLR methods represent the solution in terms of time-evolving SVD-like factors of angle and space. Although previous work has explored DLR methods for TRT, most of the methods have limitations that make them impractical for realistic scenarios and uncompetitive with current non-DLR production codes. Here we develop new PN-like and SN-like Dynamic Low Rank (DLR) methods for TRT. In the SN-like DLR method, we use the time-evolving angular basis functions to select…
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Taxonomy
TopicsRadiative Heat Transfer Studies · Model Reduction and Neural Networks · Gas Dynamics and Kinetic Theory
